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Adding a second AI agent to a project feels like doubling your output. In practice, it usually means doubling your bookkeeping too. Every agent needs its own worktree so it can work without touching the branch someone else, human or otherwise, is using. Multiply that by five agents across three repos, and the isolation that made parallel work possible starts generating its own kind of work: which worktree goes with which branch, which ones are stale, which upstream nobody remembers creating.

Most teams adopting AI agents are making a bet on which one wins. Claude or Codex, Copilot or something newer next quarter. That bet is the wrong one to make. The agent you use will keep changing. The workflow around it is what actually needs to hold up.

The frontier models have led the pack for a while now. It seems like the big players of Anthropic and OpenAI keep leapfrogging each other by a couple points in benchmark scores every other month. But, a trend we are starting to see is that open weight models are improving by leaps and bounds. They don’t hold the lead and probably won’t for a while, but the fact that open models are scaring the leaders is something to think about.

GitKraken Desktop 12.4 gives developers running several worktrees and AI agent sessions a single graph to see it all, plus an inline way to approve or deny what each agent wants to do. It ships August 4, 2026, with updates carried forward from 12.1 through 12.4.

Every so often we sit down with someone from our support team and turn their week into a blog post. First up: Roberto Vizcarra, on four things generating tickets lately, AI credits, student plans, integrations, and Mac performance. Here’s what changed and what to do about it.

A faster car doesn’t get you home faster if the freeway is still jammed. That is the problem most teams run into once they add a second, third, or fourth AI coding agent to the mix. More agents generate more code. They do not automatically generate more finished work, because someone still has to track which agent is waiting on input, which one just opened a pull request, and which one has been quietly stuck for twenty minutes. Kepler is GitKraken’s answer to that traffic jam.

Most PR descriptions leave out the one thing a reviewer actually needs: why the change was made. And AI review bots that live natively inside GitHub tend to solve that with noise instead, dropping comments a reviewer then has to sort through to find the two that matter.

If you haven’t heard about our MCP server, you should really check it out. It’s probably the best way to give your agents access to the power of GitKraken’s integrations and features. Our MCP tools also help your agents understand your codebase in a way that we think lowers your token usage and improves their output.

Five coding agents sounds like leverage right up until a developer is the one keeping track of all five: one fixing a bug, one building a feature, one refactoring, and two waiting on input at the same time. AI did not create that problem. It exposed a workflow problem that was always going to surface once parallel development became normal instead of occasional.

Before agents were part of the picture, GitKraken Desktop had one job: make Git make sense. Visualize the commit graph, show branches and remotes clearly, and let a developer feel confident hitting merge. That job has not changed. What changed in GitKraken Desktop 12 is what sits on top of it.